1. The Effects of Motivation on Perceived Stress: a Study of Undergraduate Biomedical Engineering Students at The University of Texas at Austin Amanda Meriwether1, Erika Patall2, Maura Borrego3,4, Diane Schallert5  The University of Texas at Austin Department of Biomedical Engineering, 2 University of Southern California Rossier School of Education 3 The University of Texas at Austin Department of Mechanical Engineering, 4 The University of Texas at Austin Department of Curriculum and Instruction 5 The University of Texas at Austin Department of Educational Psychology W Dean Keeton, Austin, TX 78712, USA
Abstract
There are not enough science, technology, engineering, and math (STEM) graduates to fill the U.S.’s STEM workforce. In fact, graduation rates for STEM degrees are decreasing. To counteract issues with student persistence in STEM, it is important to look at relationships among malleable predictors of persistence. Toward this goal, I am studying the relationship between motivation and stress in engineering students, given that stress is a predictor for persistence. In my study of 180 undergraduate biomedical engineering (BME) students at The University of Texas at Austin, I found that amotivation was positively correlated with perceived stress for male students and underclassmen. Results suggest that it is important to begin considering what changes can be made to develop skills to reduce feelings of incompetence and improve students’ sense of autonomy and community in the male and underclassmen BME populations.
1. Introduction
Low graduation rates of students seeking STEM degrees1 and an expected increase in STEM jobs2 will lead to an expected gap of one million people in the STEM workforce2. These graduation rates depend on enrollment and retention rates, which vary by major and gender. Importantly, fewer females enroll in and matriculate with STEM degrees than males3. Understanding students’ motivation for STEM coursework is likely to inform understanding of student matriculation and retention in STEM majors.
Namely, in line with years of motivation research across contexts4, I suspect that students who experience more autonomous forms of motivation (interest, personal value) as opposed to controlled forms of motivation (e.g., pressure, tension, guilt) or amotivation toward their STEM coursework may experience less perceived stress and in turn, may be more likely to persist in STEM majors. Understanding the relationship between motivation and stress and how these variables are moderated is important to revealing potential predictors of persistence in STEM, as stress is one critical predictor of persistence. Such knowledge of upstream predictors of persistence will help inform targeted interventions to increase persistence in STEM, and particularly engineering majors.
2. Methods
193 of 500 UT BME undergraduate students participated in the study by returning the completed study survey (38.6% response rate). Thirteen surveys were discarded due to missing data, resulting in N = 180 usable responses.
The survey included basic demographic information, work, degree pursuit, involvement in the biomedical engineering community, and likelihood to persist in biomedical engineering. Finally, the survey included measures to assess students’ academic motivation in engineering, derived from the Academic Motivation Scale5, perceptions of the learning climate in the engineering courses, and experiences of stress in engineering courses, measured using the Perceived Stress Scale6.
3. Conclusion
Amotivation is a positive predictor of stress. Students who experience greater amounts of amotivation toward their studies in biomedical engineering experience more stress. When students lack competence, control, and belonging to a subject matter, negative outcomes emerge. Without any sort of motivation for a particular subject, individuals may find it harder to commit to completing coursework, leaving them in academic situations that produce more stress, such a cramming last minute for a test or struggling over a homework set after skipping instructional time. It may also be that these students who lack motivation for BME fail to connect with peers in their cohort and their professors, resulting in a lack of social and academic support. It seems to be critical, therefore, to further explore the environmental factors that may moderate the amotivation-stress relationship. Using these and future results to develop intervention plans for specific populations will help to reduce stress levels in students, ultimately increasing student persistence.
4. References
1. Technology, P. C. of A. on S. and. Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics. (2012).
2. Carnevale, A. P., Smith, N. & Strohl, J. Help wanted: Projections of jobs and education requirements through (2010).
3. Falkenheim, J. et al. Women, minorities, and persons with disabilities in science and engineering: (2017).
4. Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 25, 54–67 (2000).
5. Vallerand, R. J. et al. The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educ. Psychol. Meas. 52, 1003–10017 (1992).
6. Cohen, S., Kamarck, T. & Mermelstein, R. A Global Measure of Perceived Stress. Journal of Health and Social Behavior 24, 385–396 (1983).
Table 1. Stress and Motivation by Gender
Note. N = 180; Stress ranges from 0 – 4, with 4 being the greatest amount of perceived stress; RAI = relative autonomy index; AUT = autonomous motivation; CONT = controlled motivation; AMOT = amotivation; RAI ranges from -21 – 42, with 42 being the greatest amount of autonomous motivation; AUT, CONT, and AMOT all range from 0 – 7, with 7 being the greatest amount of a particular type of motivation; Wilcoxon rank-sum test performed for all variables; * - significant for alpha = 0.05.
Table 2. Predictors for Perceived Stress
Note. Spearman’s correlation coefficient was calculated for non-normal variables; RAI = relative autonomy index; AUT = autonomous motivation; CONT = controlled motivation; AMOT = amotivation; BME = biomedical engineering; EXT = extrinsic motivation; INT = intrinsic motivation; Start BME = the semester a student started his/her BME coursework; * - significant for alpha = 0.05.
Table 5. Probing Academic Classification Interactions in Model 5
Note. AMOT = amotivation; AC = academic classification (underclassman or upperclassman), not centered; PGC = post- graduation classification (corporate or further education); G = gender. For Model 13, upperclassmen are coded as “0”; for Model 14, underclassmen are coded as “0”.
Table 4. Probing Gender Interactions in Model 5
Note. AMOT = amotivation; AC = academic classification (underclassman or upperclassman); PGC = post-graduation classification (corporate or further education); G = gender, not centered. For Model 11, males are coded as “0”; for Model 12, females are coded as “0”.
Table 3. Centered Linear Regression Models for Perceived Stress with Amotivation
Note. AMOT = amotivation; AC = academic classification (underclassman or upperclassman); PGC = post-graduation classification (corporate or further education); G = gender.
Proceedings of the 2018 ASEE Gulf-Southwest Section Annual Conference The University of Texas at Austin
April 4-6, 2018